An important aspect of model explanations is to establish trust between humans and machine learning systems. Previous studies for explainable image classification have studied the utility of saliency maps to evaluate if a user can identify which of the two models is better [1]}. We extend this approach to object detection by running D-RISE on the public implementations of YOLOv3 and YOLOv3-Tiny, which have a mAP 55.3 and 33.1 respectively on the MS-COCO test set. We selected 242 unique objects where both detectors made the correct prediction. We then asked users to identify which of the two explanations was better, given the object of interest in the image and the two D-RISE saliency masks overlayed on the full image.
